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Internet of things-based digital scale to detect stunting symptoms in babies under two years of age

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 14 No. 3 (2024)

DOI
10.11591/ijece.v14i3.pp3467-3474
ISSN
2722-2578, 2088-8708
Published
2024-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Given the ongoing global challenge of stunting, characterized primarily by chronic underweight in infants under two years of age, a new approach leveraging digital scale and the internet of things (IoT) has been developed. This innovative system was designed to facilitate the early detection and continual monitoring of stunting symptoms caused by malnourishment. Key features include an IoT-enabled digital scale for precise weight measurement, a robust cloud platform for reliable data storage and comprehensive analysis, and an easy-to-use mobile app for user engagement. This system demonstrates its potential to simplify tracking fluctuations in baby weight and development progress related to stunting over time. Early trials demonstrated an impressive accuracy rate of 99.4% in body weight measurements and provided excellent conclusions in determining the body weight status of the infants. Overall, this IoT-based solution catalyzes the improvement of stunting detection methodologies and early intervention strategies, thus promising a better solution and a significant positive impact on global child health.

Collaboration network of the authors

7 authors · 13 co-authorship links · 4 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Binus University
  • Universiti Brunei Darussalam
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Rudy SusantoBinus University1
Bryan PrasetyaBinus University1
Barry LinandoBinus University1
Senanayake Mudiyanselage Namal SenanayakeUniversiti Brunei Darussalam1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 1 Nov 2026.
Metadata as oai_dc, converted from Crossref
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